Overview

Dataset statistics

Number of variables19
Number of observations120997
Missing cells16623
Missing cells (%)0.7%
Duplicate rows688
Duplicate rows (%)0.6%
Total size in memory42.6 MiB
Average record size in memory369.5 B

Variable types

NUM13
CAT3
BOOL2
DATE1

Warnings

Dataset has 688 (0.6%) duplicate rows Duplicates
artists has a high cardinality: 26731 distinct values High cardinality
id has a high cardinality: 119959 distinct values High cardinality
name has a high cardinality: 95654 distinct values High cardinality
duration_ms has 1302 (1.1%) missing values Missing
name has 2166 (1.8%) missing values Missing
id is uniformly distributed Uniform
name is uniformly distributed Uniform
instrumentalness has 32589 (26.9%) zeros Zeros
key has 15377 (12.7%) zeros Zeros
popularity has 18138 (15.0%) zeros Zeros

Reproduction

Analysis started2021-03-21 15:12:46.776339
Analysis finished2021-03-21 15:13:41.775228
Duration55 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

valence
Real number (ℝ≥0)

Distinct1670
Distinct (%)1.4%
Missing821
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.5495914139
Minimum0
Maximum1
Zeros74
Zeros (%)0.1%
Memory size945.4 KiB
2021-03-21T17:13:41.940787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.108
Q10.343
median0.565
Q30.768
95-th percentile0.945
Maximum1
Range1
Interquartile range (IQR)0.425

Descriptive statistics

Standard deviation0.2602681477
Coefficient of variation (CV)0.473566619
Kurtosis-1.028679745
Mean0.5495914139
Median Absolute Deviation (MAD)0.212
Skewness-0.176913468
Sum66047.69776
Variance0.06773950871
MonotocityNot monotonic
2021-03-21T17:13:42.118311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.9615790.5%
 
0.9624700.4%
 
0.9634350.4%
 
0.9643810.3%
 
0.9653290.3%
 
0.963100.3%
 
0.9662810.2%
 
0.9672600.2%
 
0.9682100.2%
 
0.9691880.2%
 
0.5591870.2%
 
0.6671760.1%
 
0.5951750.1%
 
0.7431750.1%
 
0.971750.1%
 
0.7351730.1%
 
0.4961720.1%
 
0.81720.1%
 
0.6991710.1%
 
0.7361700.1%
 
0.6461700.1%
 
0.5681690.1%
 
0.5911690.1%
 
0.771690.1%
 
0.6841690.1%
 
Other values (1645)11414194.3%
 
(Missing)8210.7%
 
ValueCountFrequency (%) 
0740.1%
 
1e-0534< 0.1%
 
0.0005371< 0.1%
 
0.0005621< 0.1%
 
0.001661< 0.1%
 
0.002981< 0.1%
 
0.004131< 0.1%
 
0.005391< 0.1%
 
0.005541< 0.1%
 
0.007161< 0.1%
 
ValueCountFrequency (%) 
14< 0.1%
 
0.9981< 0.1%
 
0.9962< 0.1%
 
0.9951< 0.1%
 
0.9943< 0.1%
 
0.9934< 0.1%
 
0.9914< 0.1%
 
0.998< 0.1%
 
0.9896< 0.1%
 
0.9887< 0.1%
 

year
Real number (ℝ≥0)

Distinct100
Distinct (%)0.1%
Missing797
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1977.834143
Minimum1921
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size945.4 KiB
2021-03-21T17:13:42.291743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1921
5-th percentile1933
Q11958
median1979
Q32000
95-th percentile2016
Maximum2020
Range99
Interquartile range (IQR)42

Descriptive statistics

Standard deviation25.66587955
Coefficient of variation (CV)0.01297676028
Kurtosis-0.9700314126
Mean1977.834143
Median Absolute Deviation (MAD)21
Skewness-0.1825419249
Sum237735664
Variance658.7373728
MonotocityNot monotonic
2021-03-21T17:13:42.482034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
197916021.3%
 
198315821.3%
 
201815811.3%
 
198115771.3%
 
198515661.3%
 
198015331.3%
 
202015291.3%
 
197615281.3%
 
198415271.3%
 
197715171.3%
 
198215161.3%
 
198615141.3%
 
201415121.2%
 
198715081.2%
 
197815021.2%
 
201014961.2%
 
197514871.2%
 
200914861.2%
 
201514851.2%
 
197314851.2%
 
197414831.2%
 
201214801.2%
 
199014771.2%
 
201114751.2%
 
198914701.2%
 
Other values (75)8228268.0%
 
ValueCountFrequency (%) 
1921920.1%
 
192241< 0.1%
 
19231240.1%
 
19241580.1%
 
19251890.2%
 
19268950.7%
 
19274980.4%
 
19288380.7%
 
19296460.5%
 
193012811.1%
 
ValueCountFrequency (%) 
202015291.3%
 
201914191.2%
 
201815811.3%
 
201714201.2%
 
201613201.1%
 
201514851.2%
 
201415121.2%
 
201314661.2%
 
201214801.2%
 
201114751.2%
 

acousticness
Real number (ℝ≥0)

Distinct4555
Distinct (%)3.8%
Missing772
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.4721146863
Minimum0
Maximum0.996
Zeros11
Zeros (%)< 0.1%
Memory size945.4 KiB
2021-03-21T17:13:42.692264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00108
Q10.0771
median0.449
Q30.861
95-th percentile0.992
Maximum0.996
Range0.996
Interquartile range (IQR)0.7839

Descriptive statistics

Standard deviation0.3749947114
Coefficient of variation (CV)0.7942873253
Kurtosis-1.597733312
Mean0.4721146863
Median Absolute Deviation (MAD)0.3877
Skewness0.08693282905
Sum56759.98816
Variance0.1406210336
MonotocityNot monotonic
2021-03-21T17:13:42.881798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.99519521.6%
 
0.99414781.2%
 
0.99311360.9%
 
0.9929730.8%
 
0.9918150.7%
 
0.997400.6%
 
0.9966450.5%
 
0.9896420.5%
 
0.9885430.4%
 
0.9875130.4%
 
0.9865010.4%
 
0.9844520.4%
 
0.9854270.4%
 
0.9824130.3%
 
0.9833890.3%
 
0.9813650.3%
 
0.983630.3%
 
0.9793360.3%
 
0.9782970.2%
 
0.9772970.2%
 
0.9752830.2%
 
0.9722700.2%
 
0.9762690.2%
 
0.9732610.2%
 
0.9662610.2%
 
Other values (4530)10560487.3%
 
(Missing)7720.6%
 
ValueCountFrequency (%) 
011< 0.1%
 
1.01e-063< 0.1%
 
1.02e-061< 0.1%
 
1.03e-061< 0.1%
 
1.05e-062< 0.1%
 
1.11e-061< 0.1%
 
1.15e-061< 0.1%
 
1.17e-061< 0.1%
 
1.2e-061< 0.1%
 
1.21e-061< 0.1%
 
ValueCountFrequency (%) 
0.9966450.5%
 
0.99519521.6%
 
0.99414781.2%
 
0.99311360.9%
 
0.9929730.8%
 
0.9918150.7%
 
0.997400.6%
 
0.9896420.5%
 
0.9885430.4%
 
0.9875130.4%
 

artists
Categorical

HIGH CARDINALITY

Distinct26731
Distinct (%)22.3%
Missing897
Missing (%)0.7%
Memory size945.4 KiB
['Francisco Canaro']
 
850
['Эрнест Хемингуэй']
 
798
['Эрих Мария Ремарк']
 
674
['Ignacio Corsini']
 
454
['Francisco Canaro', 'Charlo']
 
397
Other values (26726)
116927 
ValueCountFrequency (%) 
['Francisco Canaro']8500.7%
 
['Эрнест Хемингуэй']7980.7%
 
['Эрих Мария Ремарк']6740.6%
 
['Ignacio Corsini']4540.4%
 
['Francisco Canaro', 'Charlo']3970.3%
 
['Frank Sinatra']3860.3%
 
['The Rolling Stones']3820.3%
 
['Bob Dylan']3780.3%
 
['The Beach Boys']3680.3%
 
['Fleetwood Mac']3650.3%
 
['Johnny Cash']3320.3%
 
['The Beatles']3110.3%
 
['Elvis Presley']3070.3%
 
['Miles Davis']2990.2%
 
['Dean Martin']2900.2%
 
['Queen']2800.2%
 
['Georgette Heyer', 'Irina Salkow']2680.2%
 
['Billie Holiday']2450.2%
 
['Lead Belly']2380.2%
 
['Ella Fitzgerald']2350.2%
 
['Lata Mangeshkar']2230.2%
 
['Led Zeppelin']2170.2%
 
['The Who']2100.2%
 
['Johann Sebastian Bach', 'Glenn Gould']2090.2%
 
['Francisco Canaro', 'Ernesto Fama']2060.2%
 
Other values (26706)11117891.9%
 
(Missing)8970.7%
 
2021-03-21T17:13:43.160053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique15836 ?
Unique (%)13.2%
2021-03-21T17:13:43.360559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length479
Median length17
Mean length22.40985314
Min length3

Overview of Unicode Properties

Unique unicode characters564
Unique unicode categories18 ?
Unique unicode scripts10 ?
Unique unicode blocks12 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
'30897411.4%
 
2176078.0%
 
e1798136.6%
 
a1724596.4%
 
n1296424.8%
 
r1287404.7%
 
i1243104.6%
 
o1222084.5%
 
[1201024.4%
 
]1201024.4%
 
l889433.3%
 
s875293.2%
 
t775112.9%
 
h619572.3%
 
d445301.6%
 
u431071.6%
 
c424631.6%
 
y370841.4%
 
,359601.3%
 
m332511.2%
 
S285971.1%
 
g275431.0%
 
B267661.0%
 
T260741.0%
 
C259241.0%
 
Other values (539)40032914.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter154009356.8%
 
Other Punctuation35885213.2%
 
Uppercase Letter34653912.8%
 
Space Separator2176078.0%
 
Close Punctuation1201834.4%
 
Open Punctuation1201824.4%
 
Decimal Number40500.1%
 
Dash Punctuation20020.1%
 
Other Letter15280.1%
 
Currency Symbol321< 0.1%
 
Math Symbol74< 0.1%
 
Final Punctuation32< 0.1%
 
Nonspacing Mark28< 0.1%
 
Modifier Letter12< 0.1%
 
Initial Punctuation9< 0.1%
 
Modifier Symbol7< 0.1%
 
Other Symbol5< 0.1%
 
Connector Punctuation1< 0.1%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
[12010299.9%
 
(800.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
'30897486.1%
 
,3596010.0%
 
.53441.5%
 
&39751.1%
 
"36151.0%
 
/5620.2%
 
!2590.1%
 
*52< 0.1%
 
:29< 0.1%
 
25< 0.1%
 
\19< 0.1%
 
;16< 0.1%
 
?11< 0.1%
 
#7< 0.1%
 
2< 0.1%
 
¡1< 0.1%
 
¿1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S285978.3%
 
B267667.7%
 
T260747.5%
 
C259247.5%
 
M247887.2%
 
A177695.1%
 
D174055.0%
 
J169074.9%
 
R168344.9%
 
L165614.8%
 
P151834.4%
 
G148734.3%
 
F135813.9%
 
H120373.5%
 
O100922.9%
 
W100502.9%
 
E100292.9%
 
K94502.7%
 
N78692.3%
 
I60421.7%
 
V52571.5%
 
Y33491.0%
 
U18720.5%
 
Z17770.5%
 
Q16950.5%
 
Other values (57)57581.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e17981311.7%
 
a17245911.2%
 
n1296428.4%
 
r1287408.4%
 
i1243108.1%
 
o1222087.9%
 
l889435.8%
 
s875295.7%
 
t775115.0%
 
h619574.0%
 
d445302.9%
 
u431072.8%
 
c424632.8%
 
y370842.4%
 
m332512.2%
 
g275431.8%
 
k217261.4%
 
b163881.1%
 
p163851.1%
 
v147761.0%
 
f110150.7%
 
w102750.7%
 
z101110.7%
 
x30120.2%
 
р30050.2%
 
Other values (109)323102.1%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
]12010299.9%
 
)810.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
217607100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-197198.5%
 
291.4%
 
20.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
273718.2%
 
166516.4%
 
546011.4%
 
042310.4%
 
73408.4%
 
93398.4%
 
33067.6%
 
42917.2%
 
82736.7%
 
62165.3%
 

Most frequent Currency Symbol characters

ValueCountFrequency (%) 
$321100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+6182.4%
 
|1216.2%
 
=11.4%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
^457.1%
 
´228.6%
 
`114.3%
 

Most frequent Other Letter characters

ValueCountFrequency (%) 
603.9%
 
422.7%
 
ו322.1%
 
302.0%
 
302.0%
 
302.0%
 
281.8%
 
281.8%
 
281.8%
 
271.8%
 
261.7%
 
261.7%
 
241.6%
 
241.6%
 
231.5%
 
221.4%
 
211.4%
 
201.3%
 
191.2%
 
181.2%
 
171.1%
 
א161.0%
 
מ161.0%
 
ן161.0%
 
ה161.0%
 
Other values (264)88958.2%
 

Most frequent Nonspacing Mark characters

ValueCountFrequency (%) 
828.6%
 
828.6%
 
725.0%
 
27.1%
 
13.6%
 
13.6%
 
13.6%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_1100.0%
 

Most frequent Final Punctuation characters

ValueCountFrequency (%) 
1856.2%
 
825.0%
 
»618.8%
 

Most frequent Other Symbol characters

ValueCountFrequency (%) 
®240.0%
 
240.0%
 
120.0%
 

Most frequent Initial Punctuation characters

ValueCountFrequency (%) 
«666.7%
 
333.3%
 

Most frequent Modifier Letter characters

ValueCountFrequency (%) 
12100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin185628568.5%
 
Common82333730.4%
 
Cyrillic246830.9%
 
Greek56700.2%
 
Han868< 0.1%
 
Katakana366< 0.1%
 
Hebrew136< 0.1%
 
Thai129< 0.1%
 
Arabic32< 0.1%
 
Hiragana19< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
'30897437.5%
 
21760726.4%
 
[12010214.6%
 
]12010214.6%
 
,359604.4%
 
.53440.6%
 
&39750.5%
 
"36150.4%
 
-19710.2%
 
27370.1%
 
16650.1%
 
/5620.1%
 
54600.1%
 
04230.1%
 
7340< 0.1%
 
9339< 0.1%
 
$321< 0.1%
 
3306< 0.1%
 
4291< 0.1%
 
8273< 0.1%
 
!259< 0.1%
 
6216< 0.1%
 
)81< 0.1%
 
(80< 0.1%
 
+61< 0.1%
 
Other values (27)273< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e1798139.7%
 
a1724599.3%
 
n1296427.0%
 
r1287406.9%
 
i1243106.7%
 
o1222086.6%
 
l889434.8%
 
s875294.7%
 
t775114.2%
 
h619573.3%
 
d445302.4%
 
u431072.3%
 
c424632.3%
 
y370842.0%
 
m332511.8%
 
S285971.5%
 
g275431.5%
 
B267661.4%
 
T260741.4%
 
C259241.4%
 
M247881.3%
 
k217261.2%
 
A177691.0%
 
D174050.9%
 
J169070.9%
 
Other values (89)24923913.4%
 

Most frequent Thai characters

ValueCountFrequency (%) 
1511.6%
 
1511.6%
 
97.0%
 
97.0%
 
97.0%
 
86.2%
 
86.2%
 
75.4%
 
75.4%
 
75.4%
 
75.4%
 
43.1%
 
32.3%
 
32.3%
 
21.6%
 
21.6%
 
21.6%
 
21.6%
 
10.8%
 
10.8%
 
10.8%
 
10.8%
 
10.8%
 
10.8%
 
10.8%
 
Other values (3)32.3%
 

Most frequent Katakana characters

ValueCountFrequency (%) 
4211.5%
 
308.2%
 
287.7%
 
277.4%
 
236.3%
 
226.0%
 
215.7%
 
113.0%
 
102.7%
 
102.7%
 
92.5%
 
82.2%
 
71.9%
 
71.9%
 
71.9%
 
71.9%
 
61.6%
 
61.6%
 
61.6%
 
61.6%
 
61.6%
 
61.6%
 
51.4%
 
51.4%
 
51.4%
 
Other values (25)4612.6%
 

Most frequent Greek characters

ValueCountFrequency (%) 
ς5249.2%
 
α5048.9%
 
ο3616.4%
 
τ3436.0%
 
ρ3125.5%
 
η2985.3%
 
ν2554.5%
 
ι2103.7%
 
λ1983.5%
 
κ1893.3%
 
σ1632.9%
 
ά1562.8%
 
ί1432.5%
 
υ1432.5%
 
Κ1262.2%
 
ώ1162.0%
 
ή1031.8%
 
μ1021.8%
 
γ981.7%
 
έ871.5%
 
ε801.4%
 
Μ791.4%
 
δ781.4%
 
π771.4%
 
Γ731.3%
 
Other values (29)85215.0%
 

Most frequent Cyrillic characters

ValueCountFrequency (%) 
р300512.2%
 
е260310.5%
 
и22279.0%
 
н18087.3%
 
м16216.6%
 
а15456.3%
 
Э14756.0%
 
т9743.9%
 
у9333.8%
 
с8613.5%
 
й8273.4%
 
г8243.3%
 
Х7983.2%
 
э7983.2%
 
к6992.8%
 
я6922.8%
 
х6822.8%
 
М6822.8%
 
Р6752.7%
 
о2351.0%
 
Т1350.5%
 
п1320.5%
 
К1230.5%
 
л540.2%
 
д420.2%
 
Other values (24)2330.9%
 

Most frequent Han characters

ValueCountFrequency (%) 
606.9%
 
303.5%
 
303.5%
 
283.2%
 
283.2%
 
263.0%
 
263.0%
 
242.8%
 
242.8%
 
202.3%
 
192.2%
 
182.1%
 
172.0%
 
161.8%
 
151.7%
 
151.7%
 
141.6%
 
131.5%
 
131.5%
 
121.4%
 
111.3%
 
111.3%
 
111.3%
 
111.3%
 
111.3%
 
Other values (164)36542.1%
 

Most frequent Hebrew characters

ValueCountFrequency (%) 
ו3223.5%
 
א1611.8%
 
מ1611.8%
 
ן1611.8%
 
ה1611.8%
 
ר85.9%
 
ל85.9%
 
ך85.9%
 
ס85.9%
 
ב85.9%
 

Most frequent Hiragana characters

ValueCountFrequency (%) 
631.6%
 
210.5%
 
210.5%
 
210.5%
 
15.3%
 
15.3%
 
15.3%
 
15.3%
 
15.3%
 
15.3%
 
15.3%
 

Most frequent Arabic characters

ValueCountFrequency (%) 
م825.0%
 
ح412.5%
 
د412.5%
 
ف412.5%
 
و412.5%
 
ز412.5%
 
ي412.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII267262398.6%
 
Cyrillic246830.9%
 
None125690.5%
 
CJK868< 0.1%
 
Katakana403< 0.1%
 
Hebrew136< 0.1%
 
Thai129< 0.1%
 
Punctuation60< 0.1%
 
Arabic32< 0.1%
 
Hiragana19< 0.1%
 
Dingbats2< 0.1%
 
Misc Symbols1< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
'30897411.6%
 
2176078.1%
 
e1798136.7%
 
a1724596.5%
 
n1296424.9%
 
r1287404.8%
 
i1243104.7%
 
o1222084.6%
 
[1201024.5%
 
]1201024.5%
 
l889433.3%
 
s875293.3%
 
t775112.9%
 
h619572.3%
 
d445301.7%
 
u431071.6%
 
c424631.6%
 
y370841.4%
 
,359601.3%
 
m332511.2%
 
S285971.1%
 
g275431.0%
 
B267661.0%
 
T260741.0%
 
C259241.0%
 
Other values (63)36142713.5%
 

Most frequent None characters

ValueCountFrequency (%) 
é261720.8%
 
á8686.9%
 
í6815.4%
 
ó5534.4%
 
ς5244.2%
 
α5044.0%
 
ö3632.9%
 
ο3612.9%
 
τ3432.7%
 
ρ3122.5%
 
η2982.4%
 
ü2662.1%
 
ν2552.0%
 
ñ2171.7%
 
ι2101.7%
 
λ1981.6%
 
κ1891.5%
 
σ1631.3%
 
ά1561.2%
 
ί1431.1%
 
υ1431.1%
 
Κ1261.0%
 
ώ1160.9%
 
ú1090.9%
 
ή1030.8%
 
Other values (98)275121.9%
 

Most frequent Thai characters

ValueCountFrequency (%) 
1511.6%
 
1511.6%
 
97.0%
 
97.0%
 
97.0%
 
86.2%
 
86.2%
 
75.4%
 
75.4%
 
75.4%
 
75.4%
 
43.1%
 
32.3%
 
32.3%
 
21.6%
 
21.6%
 
21.6%
 
21.6%
 
10.8%
 
10.8%
 
10.8%
 
10.8%
 
10.8%
 
10.8%
 
10.8%
 
Other values (3)32.3%
 

Most frequent Punctuation characters

ValueCountFrequency (%) 
2948.3%
 
1830.0%
 
813.3%
 
35.0%
 
23.3%
 

Most frequent Katakana characters

ValueCountFrequency (%) 
4210.4%
 
307.4%
 
286.9%
 
276.7%
 
256.2%
 
235.7%
 
225.5%
 
215.2%
 
123.0%
 
112.7%
 
102.5%
 
102.5%
 
92.2%
 
82.0%
 
71.7%
 
71.7%
 
71.7%
 
71.7%
 
61.5%
 
61.5%
 
61.5%
 
61.5%
 
61.5%
 
61.5%
 
51.2%
 
Other values (27)5613.9%
 

Most frequent Cyrillic characters

ValueCountFrequency (%) 
р300512.2%
 
е260310.5%
 
и22279.0%
 
н18087.3%
 
м16216.6%
 
а15456.3%
 
Э14756.0%
 
т9743.9%
 
у9333.8%
 
с8613.5%
 
й8273.4%
 
г8243.3%
 
Х7983.2%
 
э7983.2%
 
к6992.8%
 
я6922.8%
 
х6822.8%
 
М6822.8%
 
Р6752.7%
 
о2351.0%
 
Т1350.5%
 
п1320.5%
 
К1230.5%
 
л540.2%
 
д420.2%
 
Other values (24)2330.9%
 

Most frequent CJK characters

ValueCountFrequency (%) 
606.9%
 
303.5%
 
303.5%
 
283.2%
 
283.2%
 
263.0%
 
263.0%
 
242.8%
 
242.8%
 
202.3%
 
192.2%
 
182.1%
 
172.0%
 
161.8%
 
151.7%
 
151.7%
 
141.6%
 
131.5%
 
131.5%
 
121.4%
 
111.3%
 
111.3%
 
111.3%
 
111.3%
 
111.3%
 
Other values (164)36542.1%
 

Most frequent Hebrew characters

ValueCountFrequency (%) 
ו3223.5%
 
א1611.8%
 
מ1611.8%
 
ן1611.8%
 
ה1611.8%
 
ר85.9%
 
ל85.9%
 
ך85.9%
 
ס85.9%
 
ב85.9%
 

Most frequent Hiragana characters

ValueCountFrequency (%) 
631.6%
 
210.5%
 
210.5%
 
210.5%
 
15.3%
 
15.3%
 
15.3%
 
15.3%
 
15.3%
 
15.3%
 
15.3%
 

Most frequent Dingbats characters

ValueCountFrequency (%) 
2100.0%
 

Most frequent Misc Symbols characters

ValueCountFrequency (%) 
1100.0%
 

Most frequent Arabic characters

ValueCountFrequency (%) 
م825.0%
 
ح412.5%
 
د412.5%
 
ف412.5%
 
و412.5%
 
ز412.5%
 
ي412.5%
 

danceability
Real number (ℝ≥0)

Distinct1079
Distinct (%)0.9%
Missing772
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.5554564858
Minimum0
Maximum0.988
Zeros44
Zeros (%)< 0.1%
Memory size945.4 KiB
2021-03-21T17:13:43.565039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.263
Q10.439
median0.564
Q30.681
95-th percentile0.82
Maximum0.988
Range0.988
Interquartile range (IQR)0.242

Descriptive statistics

Standard deviation0.1687882179
Coefficient of variation (CV)0.3038729806
Kurtosis-0.3864337729
Mean0.5554564858
Median Absolute Deviation (MAD)0.121
Skewness-0.2122735347
Sum66779.756
Variance0.02848946252
MonotocityNot monotonic
2021-03-21T17:13:43.772487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.5653170.3%
 
0.613090.3%
 
0.5563070.3%
 
0.6323050.3%
 
0.6123010.2%
 
0.62970.2%
 
0.6212960.2%
 
0.5452960.2%
 
0.622940.2%
 
0.5482930.2%
 
0.6022930.2%
 
0.6232900.2%
 
0.6372890.2%
 
0.5462880.2%
 
0.6412870.2%
 
0.542860.2%
 
0.6292860.2%
 
0.7112860.2%
 
0.5932860.2%
 
0.7052860.2%
 
0.6312850.2%
 
0.5372850.2%
 
0.5552850.2%
 
0.6282840.2%
 
0.5672830.2%
 
Other values (1054)11291193.3%
 
(Missing)7720.6%
 
ValueCountFrequency (%) 
044< 0.1%
 
0.05511< 0.1%
 
0.05592< 0.1%
 
0.05691< 0.1%
 
0.05741< 0.1%
 
0.05831< 0.1%
 
0.05871< 0.1%
 
0.05891< 0.1%
 
0.0591< 0.1%
 
0.05911< 0.1%
 
ValueCountFrequency (%) 
0.9881< 0.1%
 
0.9862< 0.1%
 
0.9851< 0.1%
 
0.9831< 0.1%
 
0.984< 0.1%
 
0.9793< 0.1%
 
0.9782< 0.1%
 
0.9774< 0.1%
 
0.9761< 0.1%
 
0.9756< 0.1%
 

duration_ms
Real number (ℝ≥0)

MISSING

Distinct41823
Distinct (%)34.9%
Missing1302
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean230800.8084
Minimum6467
Maximum4270034
Zeros0
Zeros (%)0.0%
Memory size945.4 KiB
2021-03-21T17:13:43.986911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6467
5-th percentile114297.9
Q1170372
median208253
Q3262560
95-th percentile407903
Maximum4270034
Range4263567
Interquartile range (IQR)92188

Descriptive statistics

Standard deviation121685.0972
Coefficient of variation (CV)0.5272299437
Kurtosis120.4495804
Mean230800.8084
Median Absolute Deviation (MAD)44280
Skewness7.032753749
Sum2.762570276e+10
Variance1.480726289e+10
MonotocityNot monotonic
2021-03-21T17:13:44.163466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
17000043< 0.1%
 
18000040< 0.1%
 
18600040< 0.1%
 
19200038< 0.1%
 
17800036< 0.1%
 
19500035< 0.1%
 
17500035< 0.1%
 
16700034< 0.1%
 
16900033< 0.1%
 
18400033< 0.1%
 
24000033< 0.1%
 
18200033< 0.1%
 
18100032< 0.1%
 
17600032< 0.1%
 
20000031< 0.1%
 
19800031< 0.1%
 
17700031< 0.1%
 
16800031< 0.1%
 
21200030< 0.1%
 
19900030< 0.1%
 
16000030< 0.1%
 
17100030< 0.1%
 
18800030< 0.1%
 
17200029< 0.1%
 
19300029< 0.1%
 
Other values (41798)11886698.2%
 
(Missing)13021.1%
 
ValueCountFrequency (%) 
64671< 0.1%
 
88531< 0.1%
 
119731< 0.1%
 
153071< 0.1%
 
166401< 0.1%
 
166531< 0.1%
 
175331< 0.1%
 
176671< 0.1%
 
178671< 0.1%
 
181511< 0.1%
 
ValueCountFrequency (%) 
42700341< 0.1%
 
38163731< 0.1%
 
35699331< 0.1%
 
35579551< 0.1%
 
35511521< 0.1%
 
35509742< 0.1%
 
35482271< 0.1%
 
35236191< 0.1%
 
34997741< 0.1%
 
33306131< 0.1%
 

energy
Real number (ℝ≥0)

Distinct2143
Distinct (%)1.8%
Missing772
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.5081473255
Minimum0
Maximum1
Zeros5
Zeros (%)< 0.1%
Memory size945.4 KiB
2021-03-21T17:13:44.367900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0899
Q10.284
median0.506
Q30.73
95-th percentile0.932
Maximum1
Range1
Interquartile range (IQR)0.446

Descriptive statistics

Standard deviation0.2663488586
Coefficient of variation (CV)0.5241567656
Kurtosis-1.107489417
Mean0.5081473255
Median Absolute Deviation (MAD)0.223
Skewness0.01222815214
Sum61092.01221
Variance0.07094171446
MonotocityNot monotonic
2021-03-21T17:13:44.557945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.3411740.1%
 
0.5281740.1%
 
0.7261710.1%
 
0.7211710.1%
 
0.1871680.1%
 
0.4971680.1%
 
0.321670.1%
 
0.21670.1%
 
0.2541650.1%
 
0.4591640.1%
 
0.5371630.1%
 
0.5341630.1%
 
0.3251620.1%
 
0.331620.1%
 
0.6751600.1%
 
0.7161600.1%
 
0.7241590.1%
 
0.5631590.1%
 
0.3951590.1%
 
0.5681580.1%
 
0.3881580.1%
 
0.4571580.1%
 
0.6471580.1%
 
0.6861570.1%
 
0.2741570.1%
 
Other values (2118)11614396.0%
 
(Missing)7720.6%
 
ValueCountFrequency (%) 
05< 0.1%
 
2e-051< 0.1%
 
2.01e-054< 0.1%
 
2.03e-054< 0.1%
 
2.8e-051< 0.1%
 
3.22e-051< 0.1%
 
6.19e-053< 0.1%
 
7.33e-051< 0.1%
 
9.9e-051< 0.1%
 
0.000111< 0.1%
 
ValueCountFrequency (%) 
113< 0.1%
 
0.99915< 0.1%
 
0.99829< 0.1%
 
0.99742< 0.1%
 
0.99646< 0.1%
 
0.99559< 0.1%
 
0.99460< 0.1%
 
0.99357< 0.1%
 
0.99251< 0.1%
 
0.991710.1%
 

explicit
Boolean

Distinct2
Distinct (%)< 0.1%
Missing772
Missing (%)0.6%
Memory size945.4 KiB
0
110847 
1
 
9378
(Missing)
 
772
ValueCountFrequency (%) 
011084791.6%
 
193787.8%
 
(Missing)7720.6%
 
2021-03-21T17:13:44.684577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct119959
Distinct (%)100.0%
Missing1038
Missing (%)0.9%
Memory size945.4 KiB
3ag2HcoefQCU7Rj2g5aQWY
 
1
6OKhBvddAlWxxFnjbpilhu
 
1
4usEz9IY9TlblkkK7D0ulC
 
1
3TieIQTlipul7oxGldyX09
 
1
3TZqmrH1D098hf7T7vCkDr
 
1
Other values (119954)
119954 
ValueCountFrequency (%) 
3ag2HcoefQCU7Rj2g5aQWY1< 0.1%
 
6OKhBvddAlWxxFnjbpilhu1< 0.1%
 
4usEz9IY9TlblkkK7D0ulC1< 0.1%
 
3TieIQTlipul7oxGldyX091< 0.1%
 
3TZqmrH1D098hf7T7vCkDr1< 0.1%
 
4Dc7pLeInhJGf3WLDAxMhe1< 0.1%
 
0uDhQ8frHlrnzqy4rcYkZI1< 0.1%
 
4qOPQ3DdC8tH7EtxOVI2gW1< 0.1%
 
7EtOFVnHpg6Czxb7pwG2j01< 0.1%
 
0FNjfOLEQCzJep0knThF2R1< 0.1%
 
1Jc3tGa11ITNgph0yewlqn1< 0.1%
 
5KTppPYHq1umr1ACDBnEg81< 0.1%
 
17nQsSXT5w3sUqwkRU1dDc1< 0.1%
 
5l3gAGbkXFfPWxh4a3J8mp1< 0.1%
 
1r3XK0QfbUJcBFprF6e6PR1< 0.1%
 
7sj71Y12St8KlvMpBt4K271< 0.1%
 
7xfJy6LXKKOrtn82TJsIoM1< 0.1%
 
1ZOBW11tFpgV0XAw8GG58T1< 0.1%
 
7Ck6PMxvakrTokpfvH6MzG1< 0.1%
 
2oDBfOYXLeIIOpSBSIfnba1< 0.1%
 
4l5hA0MIWS6mqlGrtt3keA1< 0.1%
 
19ScoKGqnfUggyqOVQjsoH1< 0.1%
 
1GFGcPhn4h9q57gJ8RXC8o1< 0.1%
 
5crrQEY0sSLPA5yaP9oedw1< 0.1%
 
1T45V6RDj1vTLFY6Cw4tNf1< 0.1%
 
Other values (119934)11993499.1%
 
(Missing)10380.9%
 
2021-03-21T17:13:45.317736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique119959 ?
Unique (%)100.0%
2021-03-21T17:13:45.504753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length22
Mean length21.83700422
Min length3

Overview of Unicode Properties

Unique unicode characters62
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0579722.2%
 
1571062.2%
 
2565712.1%
 
3560112.1%
 
4554092.1%
 
5552042.1%
 
6548652.1%
 
7521382.0%
 
n427881.6%
 
a418091.6%
 
L410851.6%
 
D409841.6%
 
y409091.5%
 
J408641.5%
 
t408571.5%
 
F408551.5%
 
l408411.5%
 
u408381.5%
 
C408361.5%
 
g408061.5%
 
9408041.5%
 
s407951.5%
 
O407711.5%
 
h407711.5%
 
x407701.5%
 
Other values (37)149955356.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter106114840.2%
 
Uppercase Letter105460739.9%
 
Decimal Number52645719.9%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
05797211.0%
 
15710610.8%
 
25657110.7%
 
35601110.6%
 
45540910.5%
 
55520410.5%
 
65486510.4%
 
7521389.9%
 
9408047.8%
 
8403777.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n427884.0%
 
a418093.9%
 
y409093.9%
 
t408573.9%
 
l408413.8%
 
u408383.8%
 
g408063.8%
 
s407953.8%
 
h407713.8%
 
x407703.8%
 
v407653.8%
 
p407333.8%
 
b407233.8%
 
r406993.8%
 
e406953.8%
 
k406903.8%
 
d406863.8%
 
w406613.8%
 
i406523.8%
 
q406343.8%
 
f405843.8%
 
m405633.8%
 
j405613.8%
 
z405303.8%
 
o404963.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L410853.9%
 
D409843.9%
 
J408643.9%
 
F408553.9%
 
C408363.9%
 
O407713.9%
 
M407683.9%
 
W406513.9%
 
Q406323.9%
 
B405933.8%
 
U405593.8%
 
G405493.8%
 
P405433.8%
 
H405353.8%
 
I405113.8%
 
A405043.8%
 
Z404953.8%
 
Y404843.8%
 
T404633.8%
 
X404353.8%
 
N404243.8%
 
R403663.8%
 
E403643.8%
 
S401863.8%
 
V401553.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin211575580.1%
 
Common52645719.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
05797211.0%
 
15710610.8%
 
25657110.7%
 
35601110.6%
 
45540910.5%
 
55520410.5%
 
65486510.4%
 
7521389.9%
 
9408047.8%
 
8403777.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n427882.0%
 
a418092.0%
 
L410851.9%
 
D409841.9%
 
y409091.9%
 
J408641.9%
 
t408571.9%
 
F408551.9%
 
l408411.9%
 
u408381.9%
 
C408361.9%
 
g408061.9%
 
s407951.9%
 
O407711.9%
 
h407711.9%
 
x407701.9%
 
M407681.9%
 
v407651.9%
 
p407331.9%
 
b407231.9%
 
r406991.9%
 
e406951.9%
 
k406901.9%
 
d406861.9%
 
w406611.9%
 
Other values (27)109275651.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2642212100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0579722.2%
 
1571062.2%
 
2565712.1%
 
3560112.1%
 
4554092.1%
 
5552042.1%
 
6548652.1%
 
7521382.0%
 
n427881.6%
 
a418091.6%
 
L410851.6%
 
D409841.6%
 
y409091.5%
 
J408641.5%
 
t408571.5%
 
F408551.5%
 
l408411.5%
 
u408381.5%
 
C408361.5%
 
g408061.5%
 
9408041.5%
 
s407951.5%
 
O407711.5%
 
h407711.5%
 
x407701.5%
 
Other values (37)149955356.8%
 

instrumentalness
Real number (ℝ≥0)

ZEROS

Distinct5395
Distinct (%)4.5%
Missing772
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.1587519611
Minimum0
Maximum1
Zeros32589
Zeros (%)26.9%
Memory size945.4 KiB
2021-03-21T17:13:45.686351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.000216
Q30.0833
95-th percentile0.901
Maximum1
Range1
Interquartile range (IQR)0.0833

Descriptive statistics

Standard deviation0.305353
Coefficient of variation (CV)1.923459704
Kurtosis1.241313059
Mean0.1587519611
Median Absolute Deviation (MAD)0.000216
Skewness1.712322121
Sum19085.95452
Variance0.09324045462
MonotocityNot monotonic
2021-03-21T17:13:45.885817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03258926.9%
 
0.9041350.1%
 
0.9161350.1%
 
0.9221280.1%
 
0.8931280.1%
 
0.9131260.1%
 
0.9081220.1%
 
0.9091210.1%
 
0.8941190.1%
 
0.9111190.1%
 
0.8921190.1%
 
0.9051190.1%
 
0.9191180.1%
 
0.9171170.1%
 
0.9011170.1%
 
0.9141160.1%
 
0.8891150.1%
 
0.931150.1%
 
0.9121150.1%
 
0.9031150.1%
 
0.9261140.1%
 
0.9021130.1%
 
0.9181120.1%
 
0.8881120.1%
 
0.8971120.1%
 
Other values (5370)8477470.1%
 
(Missing)7720.6%
 
ValueCountFrequency (%) 
03258926.9%
 
1e-0615< 0.1%
 
1.01e-0644< 0.1%
 
1.02e-0656< 0.1%
 
1.03e-0649< 0.1%
 
1.04e-0636< 0.1%
 
1.05e-0644< 0.1%
 
1.06e-0638< 0.1%
 
1.07e-0644< 0.1%
 
1.08e-0645< 0.1%
 
ValueCountFrequency (%) 
13< 0.1%
 
0.9996< 0.1%
 
0.9983< 0.1%
 
0.9971< 0.1%
 
0.9964< 0.1%
 
0.9951< 0.1%
 
0.9946< 0.1%
 
0.99310< 0.1%
 
0.9926< 0.1%
 
0.9912< 0.1%
 

key
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)< 0.1%
Missing772
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean5.196340195
Minimum0
Maximum11
Zeros15377
Zeros (%)12.7%
Memory size945.4 KiB
2021-03-21T17:13:46.056397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.52061544
Coefficient of variation (CV)0.677518274
Kurtosis-1.275423688
Mean5.196340195
Median Absolute Deviation (MAD)3
Skewness0.006413730825
Sum624730
Variance12.39473308
MonotocityNot monotonic
2021-03-21T17:13:46.188014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
01537712.7%
 
71474612.2%
 
21346011.1%
 
91279510.6%
 
5112669.3%
 
492327.6%
 
189367.4%
 
1082456.8%
 
1176206.3%
 
873996.1%
 
661105.0%
 
350394.2%
 
(Missing)7720.6%
 
ValueCountFrequency (%) 
01537712.7%
 
189367.4%
 
21346011.1%
 
350394.2%
 
492327.6%
 
5112669.3%
 
661105.0%
 
71474612.2%
 
873996.1%
 
91279510.6%
 
ValueCountFrequency (%) 
1176206.3%
 
1082456.8%
 
91279510.6%
 
873996.1%
 
71474612.2%
 
661105.0%
 
5112669.3%
 
492327.6%
 
350394.2%
 
21346011.1%
 

liveness
Real number (ℝ≥0)

Distinct1730
Distinct (%)1.4%
Missing772
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.20575007
Minimum0
Maximum1
Zeros5
Zeros (%)< 0.1%
Memory size945.4 KiB
2021-03-21T17:13:46.345631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0586
Q10.0979
median0.135
Q30.262
95-th percentile0.613
Maximum1
Range1
Interquartile range (IQR)0.1641

Descriptive statistics

Standard deviation0.1760415996
Coefficient of variation (CV)0.8556089413
Kurtosis5.025890027
Mean0.20575007
Median Absolute Deviation (MAD)0.0535
Skewness2.159603666
Sum24736.30217
Variance0.03099064479
MonotocityNot monotonic
2021-03-21T17:13:46.532136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.11112681.0%
 
0.1111531.0%
 
0.10811400.9%
 
0.10911380.9%
 
0.10710700.9%
 
0.10610220.8%
 
0.10510190.8%
 
0.11210130.8%
 
0.1039880.8%
 
0.1149780.8%
 
0.1139630.8%
 
0.1029590.8%
 
0.1049450.8%
 
0.1019320.8%
 
0.1158660.7%
 
0.1168440.7%
 
0.1177900.7%
 
0.1197530.6%
 
0.1187480.6%
 
0.127200.6%
 
0.1227050.6%
 
0.1236690.6%
 
0.1216670.6%
 
0.1246100.5%
 
0.1275920.5%
 
Other values (1705)9767380.7%
 
(Missing)7720.6%
 
ValueCountFrequency (%) 
05< 0.1%
 
0.009671< 0.1%
 
0.01011< 0.1%
 
0.01161< 0.1%
 
0.0121< 0.1%
 
0.01231< 0.1%
 
0.01363< 0.1%
 
0.01391< 0.1%
 
0.01461< 0.1%
 
0.01481< 0.1%
 
ValueCountFrequency (%) 
11< 0.1%
 
0.9991< 0.1%
 
0.9981< 0.1%
 
0.9974< 0.1%
 
0.9962< 0.1%
 
0.9959< 0.1%
 
0.9947< 0.1%
 
0.9935< 0.1%
 
0.99211< 0.1%
 
0.99113< 0.1%
 

loudness
Real number (ℝ)

Distinct23024
Distinct (%)19.2%
Missing772
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean-11.07563229
Minimum-60
Maximum3.744
Zeros0
Zeros (%)0.0%
Memory size945.4 KiB
2021-03-21T17:13:46.728572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-21.302
Q1-14.05
median-10.208
Q3-6.966
95-th percentile-4.05
Maximum3.744
Range63.744
Interquartile range (IQR)7.084

Descriptive statistics

Standard deviation5.485659461
Coefficient of variation (CV)-0.4952908618
Kurtosis2.088603204
Mean-11.07563229
Median Absolute Deviation (MAD)3.48
Skewness-1.09497212
Sum-1331567.892
Variance30.09245973
MonotocityNot monotonic
2021-03-21T17:13:46.918135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-8.3224< 0.1%
 
-7.74422< 0.1%
 
-7.0222< 0.1%
 
-6.94221< 0.1%
 
-7.63221< 0.1%
 
-6.66421< 0.1%
 
-10.86620< 0.1%
 
-9.13120< 0.1%
 
-9.23620< 0.1%
 
-4.89320< 0.1%
 
-7.59819< 0.1%
 
-7.07619< 0.1%
 
-9.44819< 0.1%
 
-6.08319< 0.1%
 
-7.56619< 0.1%
 
-7.43619< 0.1%
 
-10.3419< 0.1%
 
-8.72219< 0.1%
 
-11.1519< 0.1%
 
-11.71619< 0.1%
 
-7.33419< 0.1%
 
-8.78919< 0.1%
 
-9.99919< 0.1%
 
-10.59419< 0.1%
 
-6.51919< 0.1%
 
Other values (22999)11972999.0%
 
(Missing)7720.6%
 
ValueCountFrequency (%) 
-605< 0.1%
 
-551< 0.1%
 
-54.8371< 0.1%
 
-52.221< 0.1%
 
-51.1231< 0.1%
 
-51.081< 0.1%
 
-48.5871< 0.1%
 
-48.2782< 0.1%
 
-47.4521< 0.1%
 
-47.191< 0.1%
 
ValueCountFrequency (%) 
3.7441< 0.1%
 
1.9631< 0.1%
 
1.831< 0.1%
 
1.4831< 0.1%
 
1.3421< 0.1%
 
1.2751< 0.1%
 
1.0731< 0.1%
 
0.8781< 0.1%
 
0.6741< 0.1%
 
0.5231< 0.1%
 

mode
Boolean

Distinct2
Distinct (%)< 0.1%
Missing772
Missing (%)0.6%
Memory size945.4 KiB
1
84922 
0
35303 
(Missing)
 
772
ValueCountFrequency (%) 
18492270.2%
 
03530329.2%
 
(Missing)7720.6%
 
2021-03-21T17:13:47.052785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

name
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct95654
Distinct (%)80.5%
Missing2166
Missing (%)1.8%
Memory size945.4 KiB
Winter Wonderland
 
56
Summertime
 
34
Jingle Bells
 
34
Sleigh Ride
 
29
White Christmas
 
28
Other values (95649)
118650 
ValueCountFrequency (%) 
Winter Wonderland56< 0.1%
 
Summertime34< 0.1%
 
Jingle Bells34< 0.1%
 
Sleigh Ride29< 0.1%
 
White Christmas28< 0.1%
 
Hold On27< 0.1%
 
Home27< 0.1%
 
The Christmas Song27< 0.1%
 
You27< 0.1%
 
Overture27< 0.1%
 
Angel25< 0.1%
 
Runaway24< 0.1%
 
I Love You23< 0.1%
 
'Round Midnight23< 0.1%
 
Forever23< 0.1%
 
Fever23< 0.1%
 
Alone22< 0.1%
 
Silver Bells21< 0.1%
 
Stay21< 0.1%
 
Blue Moon20< 0.1%
 
Goodbye20< 0.1%
 
Heaven20< 0.1%
 
Tonight19< 0.1%
 
Changes19< 0.1%
 
St. Louis Blues18< 0.1%
 
Other values (95629)11819497.7%
 
(Missing)21661.8%
 
2021-03-21T17:13:47.480775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique82681 ?
Unique (%)69.6%
2021-03-21T17:13:47.683284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length203
Median length18
Mean length22.71890212
Min length1

Overview of Unicode Properties

Unique unicode characters1416
Unique unicode categories21 ?
Unique unicode scripts12 ?
Unique unicode blocks17 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
39653514.4%
 
e2403628.7%
 
a1724366.3%
 
o1605815.8%
 
n1363005.0%
 
i1311934.8%
 
r1214044.4%
 
t1170944.3%
 
s861783.1%
 
l816053.0%
 
h611692.2%
 
u586492.1%
 
d563462.0%
 
m479481.7%
 
g352871.3%
 
T350941.3%
 
y350221.3%
 
S338561.2%
 
M322621.2%
 
c304411.1%
 
I264821.0%
 
-264071.0%
 
A257640.9%
 
L249480.9%
 
R234830.9%
 
Other values (1391)55207320.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter174193963.4%
 
Uppercase Letter42984215.6%
 
Space Separator39653514.4%
 
Other Punctuation664412.4%
 
Decimal Number609982.2%
 
Dash Punctuation264451.0%
 
Close Punctuation108110.4%
 
Open Punctuation107830.4%
 
Other Letter43930.2%
 
Final Punctuation230< 0.1%
 
Nonspacing Mark144< 0.1%
 
Currency Symbol101< 0.1%
 
Modifier Letter80< 0.1%
 
Math Symbol66< 0.1%
 
Initial Punctuation64< 0.1%
 
Connector Punctuation20< 0.1%
 
Other Symbol10< 0.1%
 
Modifier Symbol8< 0.1%
 
Other Number4< 0.1%
 
Format4< 0.1%
 
Letter Number1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11236720.3%
 
21103618.1%
 
01025916.8%
 
956689.3%
 
343577.1%
 
438416.3%
 
538036.2%
 
733195.4%
 
831865.2%
 
631625.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1803827.1%
 
'1476322.2%
 
,1332920.1%
 
:752311.3%
 
"45086.8%
 
/30334.6%
 
&21793.3%
 
!12261.8%
 
?9831.5%
 
;4080.6%
 
#1480.2%
 
*1140.2%
 
¿610.1%
 
450.1%
 
31< 0.1%
 
¡24< 0.1%
 
%10< 0.1%
 
@9< 0.1%
 
2< 0.1%
 
2< 0.1%
 
\2< 0.1%
 
2< 0.1%
 
1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
396535100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T350948.2%
 
S338567.9%
 
M322627.5%
 
I264826.2%
 
A257646.0%
 
L249485.8%
 
R234835.5%
 
B224005.2%
 
C217415.1%
 
D197074.6%
 
W176814.1%
 
O160563.7%
 
H153193.6%
 
P150583.5%
 
F145383.4%
 
G141643.3%
 
N139583.2%
 
Y120522.8%
 
E108672.5%
 
V97462.3%
 
K73111.7%
 
J55991.3%
 
U36140.8%
 
Ч21520.5%
 
Q21120.5%
 
Other values (76)38780.9%
 

Most frequent Currency Symbol characters

ValueCountFrequency (%) 
$9998.0%
 
£22.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-2640799.9%
 
260.1%
 
12< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e24036213.8%
 
a1724369.9%
 
o1605819.2%
 
n1363007.8%
 
i1311937.5%
 
r1214047.0%
 
t1170946.7%
 
s861784.9%
 
l816054.7%
 
h611693.5%
 
u586493.4%
 
d563463.2%
 
m479482.8%
 
g352872.0%
 
y350222.0%
 
c304411.7%
 
v223371.3%
 
p215681.2%
 
k187701.1%
 
f178721.0%
 
b136310.8%
 
w136130.8%
 
z75910.4%
 
а47580.3%
 
x38980.2%
 
Other values (128)458862.6%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(1043296.7%
 
[3403.2%
 
5< 0.1%
 
3< 0.1%
 
{2< 0.1%
 
1< 0.1%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)1046196.8%
 
]3383.1%
 
5< 0.1%
 
3< 0.1%
 
}3< 0.1%
 
1< 0.1%
 

Most frequent Other Letter characters

ValueCountFrequency (%) 
651.5%
 
601.4%
 
551.3%
 
551.3%
 
531.2%
 
471.1%
 
441.0%
 
431.0%
 
421.0%
 
410.9%
 
י380.9%
 
370.8%
 
350.8%
 
340.8%
 
340.8%
 
330.8%
 
320.7%
 
310.7%
 
310.7%
 
290.7%
 
290.7%
 
280.6%
 
270.6%
 
270.6%
 
270.6%
 
Other values (1044)341677.8%
 

Most frequent Modifier Letter characters

ValueCountFrequency (%) 
7796.2%
 
22.5%
 
11.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_1890.0%
 
210.0%
 

Most frequent Final Punctuation characters

ValueCountFrequency (%) 
17676.5%
 
5222.6%
 
»20.9%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
=2030.3%
 
+1928.8%
 
~913.6%
 
<57.6%
 
46.1%
 
|34.5%
 
34.5%
 
>34.5%
 

Most frequent Initial Punctuation characters

ValueCountFrequency (%) 
5078.1%
 
1218.8%
 
«23.1%
 

Most frequent Nonspacing Mark characters

ValueCountFrequency (%) 
2718.8%
 
2416.7%
 
2316.0%
 
149.7%
 
117.6%
 
106.9%
 
85.6%
 
74.9%
 
64.2%
 
́64.2%
 
42.8%
 
21.4%
 
21.4%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
´675.0%
 
`225.0%
 

Most frequent Other Symbol characters

ValueCountFrequency (%) 
°770.0%
 
®330.0%
 

Most frequent Other Number characters

ValueCountFrequency (%) 
125.0%
 
³125.0%
 
¹125.0%
 
²125.0%
 

Most frequent Format characters

ValueCountFrequency (%) 
4100.0%
 

Most frequent Letter Number characters

ValueCountFrequency (%) 
1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin212823277.4%
 
Common57259720.8%
 
Cyrillic374481.4%
 
Greek61070.2%
 
Han24550.1%
 
Thai731< 0.1%
 
Katakana587< 0.1%
 
Hiragana285< 0.1%
 
Hebrew275< 0.1%
 
Hangul105< 0.1%
 
Arabic87< 0.1%
 
Inherited10< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
39653569.3%
 
-264074.6%
 
.180383.2%
 
'147632.6%
 
,133292.3%
 
1123672.2%
 
2110361.9%
 
)104611.8%
 
(104321.8%
 
0102591.8%
 
:75231.3%
 
956681.0%
 
"45080.8%
 
343570.8%
 
438410.7%
 
538030.7%
 
733190.6%
 
831860.6%
 
631620.6%
 
/30330.5%
 
&21790.4%
 
!12260.2%
 
?9830.2%
 
;4080.1%
 
[3400.1%
 
Other values (52)14340.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e24036211.3%
 
a1724368.1%
 
o1605817.5%
 
n1363006.4%
 
i1311936.2%
 
r1214045.7%
 
t1170945.5%
 
s861784.0%
 
l816053.8%
 
h611692.9%
 
u586492.8%
 
d563462.6%
 
m479482.3%
 
g352871.7%
 
T350941.6%
 
y350221.6%
 
S338561.6%
 
M322621.5%
 
c304411.4%
 
I264821.2%
 
A257641.2%
 
L249481.2%
 
R234831.1%
 
B224001.1%
 
v223371.0%
 
Other values (118)30959114.5%
 

Most frequent Katakana characters

ValueCountFrequency (%) 
417.0%
 
376.3%
 
315.3%
 
254.3%
 
244.1%
 
233.9%
 
233.9%
 
193.2%
 
162.7%
 
152.6%
 
152.6%
 
152.6%
 
122.0%
 
111.9%
 
111.9%
 
111.9%
 
101.7%
 
101.7%
 
91.5%
 
91.5%
 
91.5%
 
91.5%
 
91.5%
 
81.4%
 
81.4%
 
Other values (45)17730.2%
 

Most frequent Hebrew characters

ValueCountFrequency (%) 
י3813.8%
 
ה269.5%
 
ו269.5%
 
ל248.7%
 
ש217.6%
 
ר186.5%
 
ב145.1%
 
א145.1%
 
ע114.0%
 
ת103.6%
 
נ82.9%
 
ם82.9%
 
מ72.5%
 
ד72.5%
 
כ62.2%
 
ג51.8%
 
ק51.8%
 
ך51.8%
 
ס41.5%
 
פ41.5%
 
ז41.5%
 
ן31.1%
 
ח20.7%
 
צ20.7%
 
ץ20.7%
 

Most frequent Han characters

ValueCountFrequency (%) 
652.6%
 
602.4%
 
552.2%
 
471.9%
 
341.4%
 
341.4%
 
331.3%
 
311.3%
 
291.2%
 
281.1%
 
271.1%
 
241.0%
 
220.9%
 
210.9%
 
210.9%
 
180.7%
 
170.7%
 
170.7%
 
170.7%
 
150.6%
 
150.6%
 
150.6%
 
150.6%
 
140.6%
 
140.6%
 
Other values (747)176772.0%
 

Most frequent Hiragana characters

ValueCountFrequency (%) 
3512.3%
 
207.0%
 
196.7%
 
165.6%
 
134.6%
 
124.2%
 
93.2%
 
93.2%
 
82.8%
 
82.8%
 
82.8%
 
82.8%
 
72.5%
 
72.5%
 
62.1%
 
62.1%
 
62.1%
 
51.8%
 
51.8%
 
51.8%
 
41.4%
 
41.4%
 
41.4%
 
41.4%
 
41.4%
 
Other values (29)5318.6%
 

Most frequent Cyrillic characters

ValueCountFrequency (%) 
а475812.7%
 
т33739.0%
 
о27537.4%
 
ь27357.3%
 
с24736.6%
 
Ч21525.7%
 
е21055.6%
 
р19585.2%
 
н16184.3%
 
и16174.3%
 
к15344.1%
 
л13563.6%
 
м12343.3%
 
у9002.4%
 
ы6951.9%
 
ф6511.7%
 
п5141.4%
 
в4251.1%
 
з4191.1%
 
г3791.0%
 
З3751.0%
 
П3661.0%
 
й3641.0%
 
б3601.0%
 
я3380.9%
 
Other values (29)19965.3%
 

Most frequent Thai characters

ValueCountFrequency (%) 
557.5%
 
537.3%
 
446.0%
 
435.9%
 
425.7%
 
324.4%
 
294.0%
 
273.7%
 
273.7%
 
273.7%
 
243.3%
 
233.1%
 
212.9%
 
212.9%
 
172.3%
 
152.1%
 
141.9%
 
141.9%
 
131.8%
 
121.6%
 
121.6%
 
111.5%
 
101.4%
 
101.4%
 
101.4%
 
Other values (28)12517.1%
 

Most frequent Greek characters

ValueCountFrequency (%) 
α66310.9%
 
ο4477.3%
 
ι4246.9%
 
τ3666.0%
 
ν3575.8%
 
ρ2904.7%
 
ε2694.4%
 
μ2373.9%
 
ά2353.8%
 
λ2313.8%
 
σ2193.6%
 
ς2133.5%
 
κ2123.5%
 
υ1903.1%
 
π1592.6%
 
έ1422.3%
 
γ1191.9%
 
ί1091.8%
 
η1061.7%
 
ό831.4%
 
ύ691.1%
 
Τ631.0%
 
δ591.0%
 
χ591.0%
 
Μ591.0%
 
Other values (35)72711.9%
 

Most frequent Inherited characters

ValueCountFrequency (%) 
́660.0%
 
440.0%
 

Most frequent Hangul characters

ValueCountFrequency (%) 
32.9%
 
32.9%
 
32.9%
 
21.9%
 
21.9%
 
21.9%
 
21.9%
 
21.9%
 
21.9%
 
21.9%
 
21.9%
 
21.9%
 
21.9%
 
21.9%
 
21.9%
 
21.9%
 
21.9%
 
21.9%
 
21.9%
 
21.9%
 
11.0%
 
11.0%
 
11.0%
 
11.0%
 
11.0%
 
Other values (57)5754.3%
 

Most frequent Arabic characters

ValueCountFrequency (%) 
ا1416.1%
 
ل1314.9%
 
ي66.9%
 
ى66.9%
 
و66.9%
 
م66.9%
 
ب44.6%
 
ن44.6%
 
ه44.6%
 
ع33.4%
 
خ33.4%
 
ح22.3%
 
ت22.3%
 
ة22.3%
 
د22.3%
 
ف22.3%
 
ق22.3%
 
ر11.1%
 
ذ11.1%
 
ز11.1%
 
ج11.1%
 
أ11.1%
 
س11.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII269279898.0%
 
Cyrillic374481.4%
 
None136490.5%
 
CJK24550.1%
 
Thai731< 0.1%
 
Katakana709< 0.1%
 
Punctuation357< 0.1%
 
Hiragana289< 0.1%
 
Hebrew275< 0.1%
 
Hangul97< 0.1%
 
Arabic87< 0.1%
 
Jamo8< 0.1%
 
Diacriticals6< 0.1%
 
Latin Ext Additional4< 0.1%
 
Arrows3< 0.1%
 
Phonetic Ext2< 0.1%
 
Number Forms1< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
39653514.7%
 
e2403628.9%
 
a1724366.4%
 
o1605816.0%
 
n1363005.1%
 
i1311934.9%
 
r1214044.5%
 
t1170944.3%
 
s861783.2%
 
l816053.0%
 
h611692.3%
 
u586492.2%
 
d563462.1%
 
m479481.8%
 
g352871.3%
 
T350941.3%
 
y350221.3%
 
S338561.3%
 
M322621.2%
 
c304411.1%
 
I264821.0%
 
-264071.0%
 
A257641.0%
 
L249480.9%
 
R234830.9%
 
Other values (69)49595218.4%
 

Most frequent Katakana characters

ValueCountFrequency (%) 
7710.9%
 
456.3%
 
415.8%
 
375.2%
 
314.4%
 
253.5%
 
243.4%
 
233.2%
 
233.2%
 
192.7%
 
162.3%
 
152.1%
 
152.1%
 
152.1%
 
121.7%
 
111.6%
 
111.6%
 
111.6%
 
101.4%
 
101.4%
 
91.3%
 
91.3%
 
91.3%
 
91.3%
 
91.3%
 
Other values (47)19327.2%
 

Most frequent None characters

ValueCountFrequency (%) 
é145510.7%
 
í7965.8%
 
ó7705.6%
 
α6634.9%
 
á6314.6%
 
ñ5213.8%
 
ο4473.3%
 
ι4243.1%
 
è3662.7%
 
τ3662.7%
 
ν3572.6%
 
ρ2902.1%
 
ε2692.0%
 
ü2641.9%
 
ö2411.8%
 
ä2381.7%
 
μ2371.7%
 
ά2351.7%
 
λ2311.7%
 
ú2211.6%
 
σ2191.6%
 
ς2131.6%
 
κ2121.6%
 
υ1901.4%
 
à1871.4%
 
Other values (142)360626.4%
 

Most frequent Punctuation characters

ValueCountFrequency (%) 
17649.3%
 
5214.6%
 
5014.0%
 
318.7%
 
267.3%
 
123.4%
 
41.1%
 
20.6%
 
20.6%
 
20.6%
 

Most frequent Phonetic Ext characters

ValueCountFrequency (%) 
2100.0%
 

Most frequent Hebrew characters

ValueCountFrequency (%) 
י3813.8%
 
ה269.5%
 
ו269.5%
 
ל248.7%
 
ש217.6%
 
ר186.5%
 
ב145.1%
 
א145.1%
 
ע114.0%
 
ת103.6%
 
נ82.9%
 
ם82.9%
 
מ72.5%
 
ד72.5%
 
כ62.2%
 
ג51.8%
 
ק51.8%
 
ך51.8%
 
ס41.5%
 
פ41.5%
 
ז41.5%
 
ן31.1%
 
ח20.7%
 
צ20.7%
 
ץ20.7%
 

Most frequent CJK characters

ValueCountFrequency (%) 
652.6%
 
602.4%
 
552.2%
 
471.9%
 
341.4%
 
341.4%
 
331.3%
 
311.3%
 
291.2%
 
281.1%
 
271.1%
 
241.0%
 
220.9%
 
210.9%
 
210.9%
 
180.7%
 
170.7%
 
170.7%
 
170.7%
 
150.6%
 
150.6%
 
150.6%
 
150.6%
 
140.6%
 
140.6%
 
Other values (747)176772.0%
 

Most frequent Hiragana characters

ValueCountFrequency (%) 
3512.1%
 
206.9%
 
196.6%
 
165.5%
 
134.5%
 
124.2%
 
93.1%
 
93.1%
 
82.8%
 
82.8%
 
82.8%
 
82.8%
 
72.4%
 
72.4%
 
62.1%
 
62.1%
 
62.1%
 
51.7%
 
51.7%
 
51.7%
 
41.4%
 
41.4%
 
41.4%
 
41.4%
 
41.4%
 
Other values (30)5719.7%
 

Most frequent Cyrillic characters

ValueCountFrequency (%) 
а475812.7%
 
т33739.0%
 
о27537.4%
 
ь27357.3%
 
с24736.6%
 
Ч21525.7%
 
е21055.6%
 
р19585.2%
 
н16184.3%
 
и16174.3%
 
к15344.1%
 
л13563.6%
 
м12343.3%
 
у9002.4%
 
ы6951.9%
 
ф6511.7%
 
п5141.4%
 
в4251.1%
 
з4191.1%
 
г3791.0%
 
З3751.0%
 
П3661.0%
 
й3641.0%
 
б3601.0%
 
я3380.9%
 
Other values (29)19965.3%
 

Most frequent Thai characters

ValueCountFrequency (%) 
557.5%
 
537.3%
 
446.0%
 
435.9%
 
425.7%
 
324.4%
 
294.0%
 
273.7%
 
273.7%
 
273.7%
 
243.3%
 
233.1%
 
212.9%
 
212.9%
 
172.3%
 
152.1%
 
141.9%
 
141.9%
 
131.8%
 
121.6%
 
121.6%
 
111.5%
 
101.4%
 
101.4%
 
101.4%
 
Other values (28)12517.1%
 

Most frequent Diacriticals characters

ValueCountFrequency (%) 
́6100.0%
 

Most frequent Hangul characters

ValueCountFrequency (%) 
33.1%
 
33.1%
 
33.1%
 
22.1%
 
22.1%
 
22.1%
 
22.1%
 
22.1%
 
22.1%
 
22.1%
 
22.1%
 
22.1%
 
22.1%
 
22.1%
 
22.1%
 
22.1%
 
22.1%
 
22.1%
 
22.1%
 
22.1%
 
11.0%
 
11.0%
 
11.0%
 
11.0%
 
11.0%
 
Other values (49)4950.5%
 

Most frequent Arrows characters

ValueCountFrequency (%) 
3100.0%
 

Most frequent Latin Ext Additional characters

ValueCountFrequency (%) 
125.0%
 
125.0%
 
125.0%
 
125.0%
 

Most frequent Arabic characters

ValueCountFrequency (%) 
ا1416.1%
 
ل1314.9%
 
ي66.9%
 
ى66.9%
 
و66.9%
 
م66.9%
 
ب44.6%
 
ن44.6%
 
ه44.6%
 
ع33.4%
 
خ33.4%
 
ح22.3%
 
ت22.3%
 
ة22.3%
 
د22.3%
 
ف22.3%
 
ق22.3%
 
ر11.1%
 
ذ11.1%
 
ز11.1%
 
ج11.1%
 
أ11.1%
 
س11.1%
 

Most frequent Jamo characters

ValueCountFrequency (%) 
112.5%
 
112.5%
 
112.5%
 
112.5%
 
112.5%
 
112.5%
 
112.5%
 
112.5%
 

Most frequent Number Forms characters

ValueCountFrequency (%) 
1100.0%
 

tempo
Real number (ℝ≥0)

Distinct59854
Distinct (%)49.8%
Missing772
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean125.9440288
Minimum0
Maximum181.368
Zeros44
Zeros (%)< 0.1%
Memory size945.4 KiB
2021-03-21T17:13:48.161561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile97.218
Q1109.854
median124.126
Q3140.402
95-th percentile171.7818
Maximum181.368
Range181.368
Interquartile range (IQR)30.548

Descriptive statistics

Standard deviation24.84405719
Coefficient of variation (CV)0.1972626842
Kurtosis0.562343962
Mean125.9440288
Median Absolute Deviation (MAD)15.348
Skewness-0.08314024628
Sum15141620.87
Variance617.2271775
MonotocityNot monotonic
2021-03-21T17:13:48.352059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
044< 0.1%
 
120.01220< 0.1%
 
12020< 0.1%
 
128.00519< 0.1%
 
120.01118< 0.1%
 
119.99417< 0.1%
 
119.99316< 0.1%
 
119.96916< 0.1%
 
128.02516< 0.1%
 
129.99516< 0.1%
 
120.00515< 0.1%
 
119.99615< 0.1%
 
127.99715< 0.1%
 
119.97315< 0.1%
 
124.99415< 0.1%
 
119.98715< 0.1%
 
119.98515< 0.1%
 
12815< 0.1%
 
129.97914< 0.1%
 
13014< 0.1%
 
119.98814< 0.1%
 
120.01714< 0.1%
 
119.99114< 0.1%
 
111.97514< 0.1%
 
119.99914< 0.1%
 
Other values (59829)11980599.0%
 
(Missing)7720.6%
 
ValueCountFrequency (%) 
044< 0.1%
 
30.9461< 0.1%
 
31.9881< 0.1%
 
32.4661< 0.1%
 
32.81< 0.1%
 
32.9411< 0.1%
 
33.3341< 0.1%
 
33.3911< 0.1%
 
33.9441< 0.1%
 
34.4961< 0.1%
 
ValueCountFrequency (%) 
181.3681< 0.1%
 
181.3611< 0.1%
 
181.3551< 0.1%
 
181.3541< 0.1%
 
181.3511< 0.1%
 
181.3482< 0.1%
 
181.3451< 0.1%
 
181.3411< 0.1%
 
181.3391< 0.1%
 
181.3371< 0.1%
 
Distinct9958
Distinct (%)8.3%
Missing1110
Missing (%)0.9%
Memory size945.4 KiB
Minimum1921-01-01 00:00:00
Maximum2020-11-24 00:00:00
2021-03-21T17:13:48.550037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:48.733548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

speechiness
Real number (ℝ≥0)

Distinct1588
Distinct (%)1.3%
Missing772
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.09322205199
Minimum0
Maximum0.97
Zeros44
Zeros (%)< 0.1%
Memory size945.4 KiB
2021-03-21T17:13:48.939546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0284
Q10.035
median0.045
Q30.0731
95-th percentile0.322
Maximum0.97
Range0.97
Interquartile range (IQR)0.0381

Descriptive statistics

Standard deviation0.1538728573
Coefficient of variation (CV)1.650605774
Kurtosis19.63374421
Mean0.09322205199
Median Absolute Deviation (MAD)0.0128
Skewness4.325743154
Sum11207.6212
Variance0.0236768562
MonotocityNot monotonic
2021-03-21T17:13:49.122573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.03374240.4%
 
0.03524170.3%
 
0.03474170.3%
 
0.03494160.3%
 
0.03344130.3%
 
0.03264090.3%
 
0.03624050.3%
 
0.03324020.3%
 
0.03313990.3%
 
0.0343980.3%
 
0.03443960.3%
 
0.03333960.3%
 
0.03483960.3%
 
0.03283950.3%
 
0.0333940.3%
 
0.03113940.3%
 
0.03433910.3%
 
0.03233900.3%
 
0.03633900.3%
 
0.03193890.3%
 
0.03153880.3%
 
0.0353870.3%
 
0.03223850.3%
 
0.03663830.3%
 
0.03413810.3%
 
Other values (1563)11027091.1%
 
(Missing)7720.6%
 
ValueCountFrequency (%) 
044< 0.1%
 
0.02231< 0.1%
 
0.02252< 0.1%
 
0.02261< 0.1%
 
0.02271< 0.1%
 
0.02282< 0.1%
 
0.02292< 0.1%
 
0.0234< 0.1%
 
0.02317< 0.1%
 
0.02327< 0.1%
 
ValueCountFrequency (%) 
0.971< 0.1%
 
0.9682< 0.1%
 
0.9678< 0.1%
 
0.96615< 0.1%
 
0.96512< 0.1%
 
0.96423< 0.1%
 
0.96334< 0.1%
 
0.96238< 0.1%
 
0.96147< 0.1%
 
0.9646< 0.1%
 

popularity
Real number (ℝ≥0)

ZEROS

Distinct99
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.35771135
Minimum0
Maximum100
Zeros18138
Zeros (%)15.0%
Memory size945.4 KiB
2021-03-21T17:13:49.300646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median34
Q349
95-th percentile66
Maximum100
Range100
Interquartile range (IQR)35

Descriptive statistics

Standard deviation21.60075601
Coefficient of variation (CV)0.667561305
Kurtosis-0.9692595652
Mean32.35771135
Median Absolute Deviation (MAD)16
Skewness-0.04687980594
Sum3915186
Variance466.5926604
MonotocityNot monotonic
2021-03-21T17:13:49.495637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01813815.0%
 
4323001.9%
 
4222521.9%
 
4022401.9%
 
4422391.9%
 
4122241.8%
 
3921491.8%
 
3621271.8%
 
3521141.7%
 
4620991.7%
 
4520541.7%
 
3720511.7%
 
3420251.7%
 
4720231.7%
 
4820201.7%
 
3819651.6%
 
4919611.6%
 
3219461.6%
 
3119291.6%
 
3319101.6%
 
3018551.5%
 
118521.5%
 
2718381.5%
 
2918351.5%
 
5018111.5%
 
Other values (74)5404044.7%
 
ValueCountFrequency (%) 
01813815.0%
 
118521.5%
 
211100.9%
 
39360.8%
 
47490.6%
 
56830.6%
 
67040.6%
 
77250.6%
 
87740.6%
 
98300.7%
 
ValueCountFrequency (%) 
1001< 0.1%
 
971< 0.1%
 
963< 0.1%
 
952< 0.1%
 
943< 0.1%
 
932< 0.1%
 
927< 0.1%
 
917< 0.1%
 
9010< 0.1%
 
8912< 0.1%
 

Interactions

2021-03-21T17:13:04.200079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:04.414374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:04.704598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:04.892106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:05.089090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:05.275590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:05.472083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:05.666082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:05.854622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:06.045110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:06.241138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:06.442597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:06.635103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:06.820616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:07.011116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:07.206856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:07.393456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:07.585973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:07.771446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:07.965926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:08.161911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:08.352914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:08.550047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:08.755543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:08.964753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:09.173624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:09.388054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:09.617948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:09.840864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:10.067256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:10.300153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:10.520159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:10.872177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:11.105070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:11.324991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:11.547399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:11.761849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:11.970849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:12.170796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:12.371851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:12.569393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:12.772609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:12.973097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:13.176576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:13.364073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:13.557592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:13.746139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:13.936215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:14.130701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:14.322210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:14.525768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:14.718922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:14.914396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:15.093916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:15.281413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:15.474922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:15.653442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:15.829977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:16.014498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:16.196012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:16.372108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:16.549185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:16.736769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:16.926225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:17.124698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:17.337139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:17.550009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:17.771421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:17.982951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:18.308591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:18.506063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:18.725483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:18.935432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:19.152378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:19.357829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:19.581232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:19.787722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:19.976687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:20.165227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:20.356677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:20.549162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:20.735704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:20.931692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:21.115264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:21.302762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:21.491259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:21.682744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:21.881214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:22.070217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:22.270683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:22.461178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:22.642695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:22.842910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:23.027925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:23.208948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:23.389369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:23.572840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:23.770313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:23.952868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:24.143980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:24.330992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:24.515523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:24.701027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:24.883546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:25.094024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:25.311223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:25.521659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:25.741073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:25.964476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:26.189429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:26.406846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:26.626772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:26.856192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:27.097546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:27.300007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:27.664032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:27.848071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:28.029586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:28.215534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:28.410109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:28.606111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:28.816301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:29.030721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:29.240161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:29.473134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:29.678059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:29.866912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:30.061891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:30.255391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:30.448693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:30.659853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:30.849853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:31.051314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:31.255767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:31.440277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:31.644760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:31.837425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:32.040874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:32.226923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:32.416416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:32.615742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:32.843140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:33.058563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:33.270996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:33.484987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:33.701175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:33.923921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:34.123398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:34.350789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:34.553249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:34.773659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:34.973124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:35.179616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:35.409051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:35.625015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:35.831776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:36.038969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:36.230455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:36.407983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:36.595075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:36.773247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:36.951805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:37.129863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:37.308478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:37.527451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:37.728865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:37.928332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:38.112872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:38.295703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-21T17:13:49.678698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-21T17:13:49.968882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-21T17:13:50.251127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-21T17:13:50.535876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-03-21T17:13:39.149558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:39.778076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:40.707899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-21T17:13:41.365192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

valenceyearacousticnessartistsdanceabilityduration_msenergyexplicitidinstrumentalnesskeylivenessloudnessmodenametemporelease_datespeechinesspopularity
00.1842019.00.434000['Swavay']0.783198577.00.34101.03g3RCV5ImXwzHpKwM2iunc0.0000987.00.3620-12.3531.02:00 AM126.7992019-05-240.072758
10.6152019.00.026700['KAYTRANADA', 'Kali Uchis']0.794186680.00.75701.041SwdQIX8Hy2u6fuEDgvWr0.0003066.00.0621-6.6440.010%107.9902019-12-130.123068
20.3681997.00.782000['Elliott Smith']0.489198973.00.08580.053q5m5Bpkr1Pin7mPKQxMi0.0001094.00.1110-20.4650.02:45 AM167.2841997-02-250.043238
30.1992017.00.003060['Casey Donahew']0.295272787.00.66300.06LjvqlPeCj62wF4oPJ6XOe0.0000007.00.1140-5.2531.03:00 AM100.0102017-10-060.033757
40.5392018.00.385000['Bazzi']0.651167019.00.65401.06pmZMP2ET1OJi5rKfLO8jD0.0000001.00.1810-5.4811.03:15141.9752018-04-120.046670
50.1632010.00.013300['Danger']0.721271947.00.93400.00lO8ZTS4kNYa9eOSC1QYQi0.4680008.00.0915-4.8321.04:30128.9992010-01-250.046752
60.4312017.00.139000['JAY-Z']0.261284493.00.85201.01gT5TGwbkkkUliNzHRIGi10.0000439.00.4770-4.9651.04:44177.9972017-07-070.158062
70.5281973.00.024100['The Who']0.432300387.00.85300.05E7AQh3drqwYcIQkcdUs5Y0.0815000.00.5570-6.6591.05:15126.4811973-10-190.039837
80.6471994.00.000306['Dream Theater']0.541331440.00.92100.01UQn05L6LCftnI9VoNy4Sp0.00120011.00.0684-5.8070.06:0099.9011994-10-030.059942
90.7332013.00.152000['J Balvin', 'Farruko']0.746243227.00.74600.03uvypVUsiIr1B0BccIcsEh0.0000005.00.2740-5.0460.06:00 AM175.9652013-09-290.102067

Last rows

valenceyearacousticnessartistsdanceabilityduration_msenergyexplicitidinstrumentalnesskeylivenessloudnessmodenametemporelease_datespeechinesspopularity
1209870.3221987.00.7670['Lee Moon Sae']0.308253240.00.2860.01XwAV7ty94aIJgRXP7CETn0.0007465.00.1270-10.8931.0사랑이 지나가면68.0491987-03-100.030131
1209880.6262016.00.1160['NCT 127']0.724178565.00.9760.05hHlmrSV6d9LFMsDA1lamE0.0000131.00.3520-3.2791.0소방차 Fire Truck110.0062016-07-100.060260
1209890.1751985.00.7160['Lee Moon Sae']0.577220053.00.2350.07ITUOEUnon9WsEGG069Ac30.0001605.00.1030-13.2401.0소녀127.8221985-11-200.032532
1209900.6572011.00.2450['SS501']0.610195693.00.8400.07oEnx3neiNUlJRwBadUhAS0.0000002.00.1540-4.5431.0애인만들기126.9922011-10-130.046863
1209910.9622009.00.0226['SUPER JUNIOR']0.706232333.00.9720.05w18nowVMRZrC5Na9Vxoth0.00000311.00.2670-2.3590.0쏘리 쏘리 Sorry, Sorry129.9462009-03-110.035261
1209920.4182017.00.0649['DAY6']0.458283160.00.6170.071WZ7yFuwxmQz5jJUpvkGv0.0000005.00.1090-3.4640.0예뻤어 You Were Beautiful167.9252017-06-070.033168
1209930.3692004.00.4590['Ryan Cabrera']0.598203546.00.4000.00jdeV5dSB3kUBRqe1xQJbh0.0000006.00.1510-10.0541.0TRUE96.9382004-06-080.024165
1209940.4151986.00.3070['Spandau Ballet']0.738365160.00.5280.04q2lRiodoQnyFO65watkse0.2230000.00.0733-9.7461.0TRUE97.3901986-01-010.028140
1209950.4001998.00.2540['George Strait']0.590214000.00.6690.05dMJFsGH8e1XfPOAUyfZGS0.0001517.00.1430-4.9751.0TRUE115.3411998-01-010.026847
1209960.3962020.00.1310['Dalex']0.660161748.00.5030.05cBrOhKDyiJF9bPGUHKkG00.0000009.00.1300-5.5011.0#NAME?175.8962020-04-200.340084